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Use Case
At scale, customer success becomes a coverage problem. There are more accounts than a CSM can meaningfully monitor, so they end up reactive, hearing about problems when customers complain or churn, not before.
We build AI customer success agents that monitor usage signals across your entire account base, flag at-risk accounts before they become churned accounts, draft proactive outreach for CSMs to review, and surface expansion opportunities in healthy accounts. Your CS team stays focused on the conversations that need them.
Tell us about your CS team and account volume.
Six components that together give your CS team visibility across the entire account base and time to act on what matters.
Combines product usage data, support ticket frequency, payment history, and engagement signals into a single health score per account. The scoring model is configured to your product, not a generic formula.
Accounts that cross a risk threshold trigger immediate alerts to the assigned CSM. The alert includes the specific signals that drove the score down, not just a red dot on a dashboard.
For each at-risk or stale account, the system drafts a check-in email referencing the account’s actual usage context, not a generic “just checking in.” The CSM reviews and sends.
Accounts with high usage in one area but gaps in others surface as expansion candidates. The system identifies the specific product gap and drafts an expansion message for the CSM to review.
Weekly digests for each CSM showing their portfolio health, accounts that improved, accounts that declined, and the events that drove each change.
Writes health scores, risk flags, and activity logs back into your CS tool: Gainsight, ChurnZero, Totango, or a CRM with CS workflows. CSMs work from the tools they already use.
From raw usage data to CSM action, the full sequence.
Usage data, support tickets, and payment data ingested
The system pulls from your product database, support platform, and billing system on a defined schedule. The combination of signals (not just one) drives accurate health scoring.
Health score calculated per account
Each account gets a score based on weighted signals you define during setup. A drop in daily active users weighted more heavily than a single support ticket, for example.
At-risk accounts flagged
Accounts below threshold (or that dropped significantly in a week) trigger a flag with the specific contributing signals. The CSM sees why, not just that it happened.
Proactive outreach drafted
For flagged accounts, the system drafts a check-in email that references the account’s actual situation: recent support issues, declining feature usage, or upcoming renewal date.
CSM reviews and sends
The CSM reviews the draft in their inbox or CS tool, edits as needed, and sends. Nothing sends without human approval. The system logs what was sent and when.
Expansion opportunities surfaced
Healthy accounts with growth signals (heavy usage of one module, team growth, new use cases in support tickets) appear in a weekly expansion queue for the CSM to act on.
The right fit is a SaaS business with more accounts than a CS team can meaningfully monitor manually.
At this scale, manual monitoring is impossible. CSMs are reactive. They hear about problems when customers complain or churn, not before. The AI shifts them to proactive.
Managers who want to know about risk before it shows up in renewal numbers. The health score dashboard gives a portfolio view and makes CSM coverage gaps visible.
CS teams measured on net revenue retention, not just churn prevention. The expansion surfacing component turns usage data into a pipeline of upsell and cross-sell opportunities.
Products that generate meaningful usage telemetry (login frequency, feature adoption, active users per account) have the signal needed to build an accurate health model.
We’d rather tell you upfront than take a project that won’t deliver the result you need.
Fewer than 50 accounts
Below 50 accounts, a good CSM can monitor the portfolio manually and a spreadsheet is sufficient. The build cost doesn’t make sense at low account counts.
Companies without product usage data
The health model is only as good as the signals feeding it. If your product doesn’t generate usage telemetry (or if you can’t access it) health scoring will rely only on support and billing signals, which is much less accurate. Fix the instrumentation problem first.
Tell us your account count, your CS tooling, and what usage data you currently have access to. We’ll reply within one business day with a rough scope and price range.